Deep Learning-based Classification of Viruses Using Transmission Electron Microscopy Images

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146546" target="_blank" >RIV/00216305:26220/22:PU146546 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/9851305" target="_blank" >https://ieeexplore.ieee.org/abstract/document/9851305</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP55681.2022.9851305" target="_blank" >10.1109/TSP55681.2022.9851305</a>

Result language
angličtina
Original language name
Deep Learning-based Classification of Viruses Using Transmission Electron Microscopy Images
Original language description
Humans have a strong urge to categorize natural organisms, and the categorization of viruses becomes more challenging. Viruses are not visible with the naked eyes, and their automatic classification based on images obtained with Transmission Electron Microscopy (TEM) can help a lot in the medical field. Their classification is more challenging due to their complicated intracellular structures and lighting conditions to capture the TEM images. The proposed architecture has been developed for the classification of the 14 different types of viruses. The dataset has been split into the training set, validation set and test set. The proposed model obtained better experimental results with 96.90% classification accuracy on the validation set and 96.10 % on the test set of unseen images. The performance of the proposed model has been compared with state-of-the-art pre-trained deep-learning models such that XceptionNet, MobileNet and DenseNet201. The model is accurate and computationally less complex, which supports faster processing suitable for microscopic cell image analysis for different medical applications.
Czech name
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Czech description
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Project
<a href="/en/project/VI04000039" target="_blank" >VI04000039: Early COVID-19 infection detection system for the safety of vulnerable groups using artificial intelligence</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
45th International Conference on Telecommunications and Signal Processing (TSP 2022). IEEE, 2022
ISBN
978-1-6654-6948-7
ISSN
—
e-ISSN
—
Number of pages
5
Pages from-to
174-178
Publisher name
IEEE
Place of publication
Prague, Czech Republic
Event location
Prague
Event date
Jul 13, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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